



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase			  =			logging.get_logger(__name__)

__UpperCAmelCase			  =			torch.device('''cpu''')
def 						UpperCamelCase      (  )      ->	Tuple:
   UpperCamelCase       :    List[str]		      =      'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase       :    Tuple		      =      Image.open(requests.get(snake_case__			,	stream=snake_case__      ).raw      )
   return im
def 						UpperCamelCase      (  snake_case__			:       str      )      ->	int:
   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]      )

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]      )

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]      )

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]      )
def 						UpperCamelCase      (  snake_case__			:       List[Any]			,	snake_case__			:       Optional[int]			,	snake_case__			:       List[Any]      )      ->	str:
   UpperCamelCase       :    Optional[int]		      =      dct.pop(snake_case__      )
   UpperCamelCase       :    int		      =      val
def 						UpperCamelCase      (  snake_case__			:       Optional[int]      )      ->	Optional[Any]:
   UpperCamelCase       :    List[str]		      =      []
   for k in state_dict.keys():
      UpperCamelCase       :    Optional[Any]		      =      k
      if ".pwconv" in k:
         UpperCamelCase       :    Dict		      =      k_new.replace('.pwconv'			,	'.point_wise_conv'      )
      if ".dwconv" in k:
         UpperCamelCase       :    Optional[Any]		      =      k_new.replace('.dwconv'			,	'.depth_wise_conv'      )
      if ".Proj." in k:
         UpperCamelCase       :    Optional[Any]		      =      k_new.replace('.Proj.'			,	'.proj.'      )
      if "patch_embed" in k_new:
         UpperCamelCase       :    Optional[Any]		      =      k_new.replace('patch_embed'			,	'swiftformer.patch_embed.patch_embedding'      )
      if "network" in k_new:
         UpperCamelCase       :    Dict		      =      k_new.split('.'      )
         if ls[2].isdigit():
            UpperCamelCase       :    Union[str, Any]		      =      'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]      )
         else:
            UpperCamelCase       :    int		      =      k_new.replace('network'			,	'swiftformer.encoder.network'      )
      rename_keys.append((k, k_new)      )
   return rename_keys
@torch.no_grad()
def 						UpperCamelCase      (  snake_case__			:       List[Any]			,	snake_case__			:       List[Any]			,	snake_case__			:       str      )      ->	List[Any]:
   UpperCamelCase       :    Dict		      =      SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   UpperCamelCase       :    str		      =      1000
   UpperCamelCase       :    List[Any]		      =      'huggingface/label-files'
   UpperCamelCase       :    str		      =      'imagenet-1k-id2label.json'
   UpperCamelCase       :    List[Any]		      =      json.load(open(hf_hub_download(snake_case__			,	snake_case__			,	repo_type='dataset'      )			,	'r'      )      )
   UpperCamelCase       :    List[Any]		      =      {int(snake_case__      ): v for k, v in idalabel.items()}
   UpperCamelCase       :    List[Any]		      =      idalabel
   UpperCamelCase       :    Dict		      =      {v: k for k, v in idalabel.items()}

   # size of the architecture
   if swiftformer_name == "swiftformer_xs":
      UpperCamelCase       :    Any		      =      [3, 3, 6, 4]
      UpperCamelCase       :    Optional[Any]		      =      [48, 56, 112, 220]

   elif swiftformer_name == "swiftformer_s":
      UpperCamelCase       :    Optional[Any]		      =      [3, 3, 9, 6]
      UpperCamelCase       :    str		      =      [48, 64, 168, 224]

   elif swiftformer_name == "swiftformer_l1":
      UpperCamelCase       :    Optional[Any]		      =      [4, 3, 10, 5]
      UpperCamelCase       :    Union[str, Any]		      =      [48, 96, 192, 384]

   elif swiftformer_name == "swiftformer_l3":
      UpperCamelCase       :    List[Any]		      =      [4, 4, 12, 6]
      UpperCamelCase       :    List[str]		      =      [64, 128, 320, 512]

   # load state_dict of original model, remove and rename some keys
   if original_ckpt:
      if original_ckpt.startswith('https'      ):
         UpperCamelCase       :    Any		      =      torch.hub.load_state_dict_from_url(snake_case__			,	map_location='cpu'			,	check_hash=snake_case__      )
      else:
         UpperCamelCase       :    Optional[Any]		      =      torch.load(snake_case__			,	map_location='cpu'      )
   UpperCamelCase       :    int		      =      checkpoint

   UpperCamelCase       :    str		      =      create_rename_keys(snake_case__      )
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(snake_case__			,	snake_case__			,	snake_case__      )

   # load HuggingFace model
   UpperCamelCase       :    str		      =      SwiftFormerForImageClassification(snake_case__      ).eval()
   hf_model.load_state_dict(snake_case__      )

   # prepare test inputs
   UpperCamelCase       :    Union[str, Any]		      =      prepare_img()
   UpperCamelCase       :    Optional[int]		      =      ViTImageProcessor.from_pretrained('preprocessor_config'      )
   UpperCamelCase       :    int		      =      processor(images=snake_case__			,	return_tensors='pt'      )

   # compare outputs from both models
   UpperCamelCase       :    Tuple		      =      get_expected_output(snake_case__      )
   UpperCamelCase       :    Optional[Any]		      =      hf_model(inputs['pixel_values']      ).logits

   assert hf_logits.shape == torch.Size([1, 1000]      )
   assert torch.allclose(hf_logits[0, 0:5]			,	snake_case__			,	atol=1E-3      )

   Path(snake_case__      ).mkdir(exist_ok=snake_case__      )
   print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""      )
   hf_model.save_pretrained(snake_case__      )


if __name__ == "__main__":
    __UpperCAmelCase			  =			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--swiftformer_name''',
        default='''swiftformer_xs''',
        choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
        type=str,
        help='''Name of the SwiftFormer model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default='''./converted_outputs/''',
        type=str,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

    __UpperCAmelCase			  =			parser.parse_args()
    convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


lowerCAmelCase_  :							List[str]             =    ['bert-base-uncased', 'bert-base-cased']
lowerCAmelCase_  :							int             =    'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():
							class 						lowerCamelCase_				(       tf.keras.Model      ):







													def __init__(	self :   Optional[int]					,     lowerCAmelCase__ :   Optional[Any]					):

																				"""simple docstring"""
																				super().__init__()
																				SCREAMING_SNAKE_CASE       :   List[str]         =      tokenizer
																				SCREAMING_SNAKE_CASE       :   str         =      AutoConfig.from_pretrained(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TFAutoModel.from_config(lowerCAmelCase__					)







													def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   Tuple					):

																				"""simple docstring"""
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.tokenizer(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   str         =      self.bert(**lowerCAmelCase__					)
																				return out["pooler_output"]
@require_tf
@require_tensorflow_text
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().setUp()

													SCREAMING_SNAKE_CASE       :   int         =      [
													    BertTokenizer.from_pretrained(lowerCAmelCase__					) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
													]  # repeat for when fast_bert_tokenizer=false
													SCREAMING_SNAKE_CASE       :   Dict         =      [TFBertTokenizer.from_pretrained(lowerCAmelCase__					) for checkpoint in TOKENIZER_CHECKPOINTS] + [
													    TFBertTokenizer.from_pretrained(lowerCAmelCase__					,     use_fast_bert_tokenizer=lowerCAmelCase__					)
													    for checkpoint in TOKENIZER_CHECKPOINTS
													]
													assert len(self.tokenizers					) == len(self.tf_tokenizers					)

													SCREAMING_SNAKE_CASE       :   Any         =      [
													    '''This is a straightforward English test sentence.''',
													    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
													    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
													    '''And some much more rare Chinese: 齉 堃 齉堃''',
													    '''Je vais aussi écrire en français pour tester les accents''',
													    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
													]
													SCREAMING_SNAKE_CASE       :   int         =      list(zip(self.test_sentences					,     self.test_sentences[::-1]					)					)







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													for tokenizer, tf_tokenizer in zip(self.tokenizers					,     self.tf_tokenizers					):
																				for test_inputs in (self.test_sentences, self.paired_sentences):
																											SCREAMING_SNAKE_CASE       :   Tuple         =      tokenizer(lowerCAmelCase__					,     return_tensors='''tf'''					,     padding='''longest'''					)
																											SCREAMING_SNAKE_CASE       :   List[Any]         =      tf_tokenizer(lowerCAmelCase__					)

																											for key in python_outputs.keys():
																																		self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape					)					)
																																		self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]					,     tf.intaa					) == tf_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf_tokenizer(self.paired_sentences					)
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf_tokenizer(
																				    text=[sentence[0] for sentence in self.paired_sentences]					,     text_pair=[sentence[1] for sentence in self.paired_sentences]					,     )
																				for key in merged_outputs.keys():
																											self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]					,     tf.intaa					) == separated_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   Any         =      tf.function(lowerCAmelCase__					)
																				for test_inputs in (self.test_sentences, self.paired_sentences):
																											SCREAMING_SNAKE_CASE       :   int         =      tf.constant(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Any         =      compiled_tokenizer(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   List[str]         =      tf_tokenizer(lowerCAmelCase__					)

																											for key in eager_outputs.keys():
																																		self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]					)					)







						@slow
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													for tf_tokenizer in self.tf_tokenizers:
																				SCREAMING_SNAKE_CASE       :   int         =      ModelToSave(tokenizer=lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Tuple         =      tf.convert_to_tensor(self.test_sentences					)
																				SCREAMING_SNAKE_CASE       :   Optional[Any]         =      model(lowerCAmelCase__					)  # Build model with some sample inputs
																				with TemporaryDirectory() as tempdir:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      Path(lowerCAmelCase__					) / '''saved.model'''
																											model.save(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      tf.keras.models.load_model(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   Tuple         =      loaded_model(lowerCAmelCase__					)
																				# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																				self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output					)					)					,     1e-5					)




